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@article{195674,
        author = {A Gayathri Priya Nandini and B NavyaSri and B Akshitha and P Anitha and S Padmapriya},
        title = {Ai based dam cleaning and waste management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {577-584},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195674},
        abstract = {Dams play a crucial role in water storage and hydroelectric power generation. However, the accumulation of floating waste such as plastics, bottles, and organic debris near dam gates significantly reduces water flow efficiency and contributes to environmental pollution. Conventional dam cleaning methods are primarily manual or mechanical, which are labor-intensive, unsafe, and unsuitable for continuous monitoring. This paper presents an AI-based dam cleaning and waste detection system that utilizes computer vision and deep learning techniques to automatically identify floating waste in real time. A camera installed near the dam captures continuous video frames, which are processed using the YOLO (You
Only Look Once) object detection algorithm. The model is trained on a dataset of waste images to detect debris with high accuracy using confidence thresholds. The system is implemented using Python, OpenCV, and deep learning frameworks such as PyTorch and Ultralytics. Upon detection, the system can activate automated cleaning mechanisms such as conveyor belts or suction pumps. This approach reduces human intervention, enhances efficiency, and promotes sustainable dam maintenance.},
        keywords = {Dam cleaning, YOLO, Computer Vision, Deep Learning, Waste Detection, Automation, Environmental Monitoring},
        month = {April},
        }